Fluid Antenna Relay Assisted Communication Systems Through Antenna Location Optimization

In this paper, we investigate the problem of resource allocation for fluid antenna relay (FAR) system with antenna location optimization. In the considered model, each user transmits information to a base station (BS) with help of FAR. The antenna location of the FAR is flexible and can be adapted to dynamic location distribution of the users. We formulate a sum rate maximization problem through jointly optimizing the antenna location and bandwidth allocation with meeting the minimum rate requirements, total bandwidth budget, and feasible antenna region constraints. To solve this problem, we obtain the optimal bandwidth in closed form. Based on the optimal bandwidth, the original problem is reduced to the antenna location optimization problem and an alternating algorithm is proposed. Simulation results verify the effectiveness of the proposed algorithm and the sum rate can be increased by up to 125% compared to the conventional schemes.

Channel Deduction: A New Learning Framework to Acquire Channel from Outdated Samples and Coarse Estimate

How to reduce the pilot overhead required for channel estimation? How to deal with the channel dynamic changes and error propagation in channel prediction? To jointly address these two critical issues in next-generation transceiver design, in this paper, we propose a novel framework named channel deduction for high-dimensional channel acquisition in multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems. Specifically, it makes use of the outdated channel information of past time slots, performs coarse estimation for the current channel with a relatively small number of pilots, and then fuses these two information to obtain a complete representation of the present channel. The rationale is to align the current channel representation to both the latent channel features within the past samples and the coarse estimate of current channel at the pilots, which, in a sense, behaves as a complementary combination of estimation and prediction and thus reduces the overall overhead. To fully exploit the highly nonlinear correlations in time, space, and frequency domains, we resort to learning-based implementation approaches. By using the highly efficient complex-domain multilayer perceptron (MLP)-mixer for crossing space-frequency domain representation and the recurrence-based or attention-based mechanisms for the past-present interaction, we respectively design two different channel deduction neural networks (CDNets). We provide a general procedure of data collection, training, and deployment to standardize the application of CDNets. Comprehensive experimental evaluations in accuracy, robustness, and efficiency demonstrate the superiority of the proposed approach, which reduces the pilot overhead by up to 88.9% compared to state-of-the-art estimation approaches and enables continuous operating even under unknown user movement and error propagation.

Secrecy Performance Analysis of RIS Assisted Ambient Backscatter Communication Networks

Reconfigurable intelligent surface (RIS) and ambient backscatter communication (AmBC) have been envisioned as two promising technologies due to their high transmission reliability as well as energy-efficiency. This paper investigates the secrecy performance of RIS assisted AmBC networks. New closed-form and asymptotic expressions of secrecy outage probability for RIS-AmBC networks are derived by taking into account both imperfect successive interference cancellation (ipSIC) and perfect SIC (pSIC) cases. On top of these, the secrecy diversity order of legitimate user is obtained in high signal-to-noise ratio region, which equals \emph{zero} and is proportional to the number of RIS elements for ipSIC and pSIC, respectively. The secrecy throughput and energy efficiency are further surveyed to evaluate the secure effectiveness of RIS-AmBC networks. Numerical results are provided to verify the accuracy of theoretical analyses and manifest that: i) The secrecy outage behavior of RIS-AmBC networks exceeds that of conventional AmBC networks; ii) Due to the mutual interference between direct and backscattering links, the number of RIS elements has an optimal value to minimise the secrecy system outage probability; and iii) Secrecy throughput and energy efficiency are strongly influenced by the reflecting coefficient and eavesdropper's wiretapping ability.

Near-Field Channel Modeling for Holographic MIMO Communications

Empowered by the latest progress on innovative metamaterials/metasurfaces and advanced antenna technologies, holographic multiple-input multiple-output (H-MIMO) emerges as a promising technology to fulfill the extreme goals of the sixth-generation (6G) wireless networks. The antenna arrays utilized in H-MIMO comprise massive (possibly to extreme extent) numbers of antenna elements, densely spaced less than half-a-wavelength and integrated into a compact space, realizing an almost continuous aperture. Thanks to the expected low cost, size, weight, and power consumption, such apertures are expected to be largely fabricated for near-field communications. In addition, the physical features of H-MIMO enable manipulations directly on the electromagnetic (EM) wave domain and spatial multiplexing. To fully leverage this potential, near-field H-MIMO channel modeling, especially from the EM perspective, is of paramount significance. In this article, we overview near-field H-MIMO channel models elaborating on the various modeling categories and respective features, as well as their challenges and evaluation criteria. We also present EM-domain channel models that address the inherit computational and measurement complexities. Finally, the article is concluded with a set of future research directions on the topic.

Low-Complexity Beam Training for Multi-RIS-Assisted Multi-User Communications

In this paper, we investigate the beam training problem in the multi-user millimeter wave (mmWave) communication system, where multiple reconfigurable intelligent surfaces (RISs) are deployed to improve the coverage and the achievable rate. However, existing beam training techniques in mmWave systems suffer from the high complexity (i.e., exponential order) and low identification accuracy. To address these problems, we propose a novel hashing multi-arm beam (HMB) training scheme that reduces the training complexity to the logarithmic order with the high accuracy. Specifically, we first design a generation mechanism for HMB codebooks. Then, we propose a demultiplexing algorithm based on the soft decision to distinguish signals from different RIS reflective links. Finally, we utilize a multi-round voting mechanism to align the beams. Simulation results show that the proposed HMB training scheme enables simultaneous training for multiple RISs and multiple users, and reduces the beam training overhead to the logarithmic level. Moreover, it also shows that our proposed scheme can significantly improve the identification accuracy by at least 20% compared to existing beam training techniques.

Coverage and Rate Analysis for Integrated Sensing and Communication Networks

Integrated sensing and communication (ISAC) is increasingly recognized as a pivotal technology for next-generation cellular networks, offering mutual benefits in both sensing and communication capabilities. This advancement necessitates a re-examination of the fundamental limits within networks where these two functions coexist via shared spectrum and infrastructures. However, traditional stochastic geometry-based performance analyses are confined to either communication or sensing networks separately. This paper bridges this gap by introducing a generalized stochastic geometry framework in ISAC networks. Based on this framework, we define and calculate the coverage and ergodic rate of sensing and communication performance under resource constraints. Then, we shed light on the fundamental limits of ISAC networks by presenting theoretical results for the coverage rate of the unified performance, taking into account the coupling effects of dual functions in coexistence networks. Further, we obtain the analytical formulations for evaluating the ergodic sensing rate constrained by the maximum communication rate, and the ergodic communication rate constrained by the maximum sensing rate. Extensive numerical results validate the accuracy of all theoretical derivations, and also indicate that denser networks significantly enhance ISAC coverage. Specifically, increasing the base station density from $1$ $\text{km}^{-2}$ to $10$ $\text{km}^{-2}$ can boost the ISAC coverage rate from $1.4\%$ to $39.8\%$. Further, results also reveal that with the increase of the constrained sensing rate, the ergodic communication rate improves significantly, but the reverse is not obvious.

Hashing Beam Training for Near-Field Communications

In this paper, we investigate the millimeter-wave (mmWave) near-field beam training problem to find the correct beam direction. In order to address the high complexity and low identification accuracy of existing beam training techniques, we propose an efficient hashing multi-arm beam (HMB) training scheme for the near-field scenario. Specifically, we first design a set of sparse bases based on the polar domain sparsity of the near-field channel. Then, the random hash functions are chosen to construct the near-field multi-arm beam training codebook. Each multi-arm beam codeword is scanned in a time slot until all the predefined codewords are traversed. Finally, the soft decision and voting methods are applied to distinguish the signal from different base stations and obtain correctly aligned beams. Simulation results show that our proposed near-field HMB training method can reduce the beam training overhead to the logarithmic level, and achieve 96.4% identification accuracy of exhaustive beam training. Moreover, we also verify applicability under the far-field scenario.

Stochastic Geometry Analysis for Distributed RISs-Assisted mmWave Communications

Millimeter wave (mmWave) has attracted considerable attention due to its wide bandwidth and high frequency. However, it is highly susceptible to blockages, resulting in significant degradation of the coverage and the sum rate. A promising approach is deploying distributed reconfigurable intelligent surfaces (RISs), which can establish extra communication links. In this paper, we investigate the impact of distributed RISs on the coverage probability and the sum rate in mmWave wireless communication systems. Specifically, we first introduce the system model, which includes the blockage, the RIS and the user distribution models, leveraging the Poisson point process. Then, we define the association criterion and derive the conditional coverage probabilities for the two cases of direct association and reflective association through RISs. Finally, we combine the two cases using Campbell's theorem and the total probability theorem to obtain the closed-form expressions for the ergodic coverage probability and the sum rate. Simulation results validate the effectiveness of the proposed analytical approach, demonstrating that the deployment of distributed RISs significantly improves the ergodic coverage probability by 45.4% and the sum rate by over 1.5 times.

Electromagnetic Hybrid Beamforming for Holographic Communications

It is well known that there is inherent radiation pattern distortion for the commercial base station antenna array, which usually needs three antenna sectors to cover the whole space. To eliminate pattern distortion and further enhance beamforming performance, we propose an electromagnetic hybrid beamforming (EHB) scheme based on a three-dimensional (3D) superdirective holographic antenna array. Specifically, EHB consists of antenna excitation current vectors (analog beamforming) and digital precoding matrices, where the implementation of analog beamforming involves the real-time adjustment of the radiation pattern to adapt it to the dynamic wireless environment. Meanwhile, the digital beamforming is optimized based on the channel characteristics of analog beamforming to further improve the achievable rate of communication systems. An electromagnetic channel model incorporating array radiation patterns and the mutual coupling effect is also developed to evaluate the benefits of our proposed scheme. Simulation results demonstrate that our proposed EHB scheme with a 3D holographic array achieves a relatively flat superdirective beamforming gain and allows for programmable focusing directions throughout the entire spatial domain. Furthermore, they also verify that the proposed scheme achieves a sum rate gain of over 150% compared to traditional beamforming algorithms.